FlipCut Supertrees: Große und akkurate Phylogenien schneller bestimmen

超树方法将输入树和重叠的类群集合组合成一棵更大、更全面的树。这里的问题是以合理的方式处理不兼容的数据。超树方法可以作为分治方法的一部分，用于系统发育树的推断：(1)我们计算，比方说，分类群的几个子集的最大似然(ML)树；(2)我们将得到的树合并为一棵超树；(3)最后，我们将该超树用作对完整的分类群集进行ML分析的种子。当前的超树方法在分析大数据集时存在速度太慢或太不准确的问题。我们最近开发了一种名为FLIPCUT超树的新型超树，它计算速度非常快，计算出的超树质量很好。在这个项目中，我们将进一步改进这种方法，使超树的质量与当前超树的“黄金标准”简约矩阵表示(MRP)不相上下。与此同时，我们希望保持该方法的非凡快捷性。然后，我们将把所得到的方法集成到一种分而治之的方法中，用于系统发育重建，并将其与“纯”ML程序进行比较。

Supertree approaches combine input trees with overlapping taxa sets into one large and more comprehensive tree. Here, the problem is dealing with incompatible data in a reasonable way. Supertree methods can be used as part of a divide-and-conquer approach for phylogenetic tree inference: (1) we compute, say, maximum likelihood (ML) trees for several subsets of taxa; (2) we merge the resulting trees into one supertree; (3) finally, we use this supertree as a seed for an ML analysis on the complete set of taxa. Current supertree methods suffer from being too slow or too inaccurate when analyzing large datasets. We have recently developed a novel supertree called FLIPCUT supertrees, which is very swift and computes supertrees of good quality. In this project, we will further advance this methods so that supertree quality is on a par with the current supertree “gold standard” Matrix Representation with Parsimony (MRP). At the same time, we want to preserve the extraordinary swiftness of the method. We will then integrate the resulting method into a divide-and-conquer approach for phylogenetic reconstruction, and evaluate it against “pure” ML programs.